Adaptive Vector-Based Sample Consensus Model for Moving Target Detection in Infrared Video

Zhang, Yunsheng; Leng, Kaijun; Park, Kyoung‐Su

doi:10.1109/lgrs.2022.3150760

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…These methods usually use artificial intelligence and computer vision methods for obstacle detection. Among these methods, the camera-based target detection algorithm is one of the most active research fields in computer vision [22][23][24][25][26], and many methods achieved some success. Unlike cameras, LiDAR sensors can provide precise distance information for objects and are already widely used.…”

Section: Railway Obstacle Detectionmentioning

confidence: 99%

Research on Railway Obstacle Detection Method Based on Developed Euclidean Clustering

et al. 2023

Electronics

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To prevent the problem of safety accidents caused by the intrusion of obstacles into railway clearance, this paper proposes an obstacle detection method based on Light Detection and Ranging (LiDAR) to obtain and process rich three-dimensional (3D) information and depth information of the railway scene. The method first preprocesses the point cloud of the railway scenario collected by LiDAR to divide a basic area containing the rails. Then, the method divides the roadbed plane and fits the rails with the random sample consensus (RANSAC) algorithm, dividing the detection area according to the position of the rails. To address the issue of over or under-segmentation in the traditional Euclidean clustering method, which is due to sparser point clouds the farther the object is from the LiDAR, this paper improves the conventional Euclidean clustering. It introduces an adaptive distance threshold to categorize obstacles. Finally, compared with traditional Euclidean clustering, K-means clustering, and density-based spatial clustering of applications with noise (DBSCAN) clustering, the improved Euclidean cluster has achieved better results in terms of computing time and segmentation accuracy. Experimental results show the ability of the method to detect railway obstacles successfully.

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Section: Railway Obstacle Detectionmentioning

confidence: 99%

Research on Railway Obstacle Detection Method Based on Developed Euclidean Clustering

et al. 2023

Electronics

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“…To detect remote flying drones, a robust switching spatial-temporal fusion (STF) segmentation model was proposed in [25] by suppressing the noises or clutters and strengthening the contrast between the target and background simultaneously. Our previous spatial-temporal local vector difference measure (STVDM) [26], which is based on frame difference method [27] and the local vector difference [28], can effectively enhance the targets and alleviate the clutters background.…”

Section: Introductionmentioning

confidence: 99%

Infrared Detection of Small-Moving Targets Using Spatial Local Vector Difference and Temporal Sample Consensus Measures

Zhang

et al. 2022

IEEE Access

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Detection small moving targets in infrared sequences with accuracy and low computation time is challenging work in infrared search and tracking systems. A frequent approach for this task is to strengthen small weak targets and diminish the clutter of background. However, the background and small targets' pixel values are close to each other and thus only a few background suppressing models are suitable to infrared small target segmentation. To efficiently resolve the problem that most of the classic approaches cannot manage low signal to noise ratio and weak objects without details, a novel spatial-temporal local difference measure is introduced for moving objects segmentation in infrared sequences. First of all, to strengthen targets, a new local vector dissimilarity measure is employed to demonstrate the difference between the weak target and their surrounding backgrounds and calculate the spatial saliency feature figure. Then the local sample consensus of succession images is used to compute the temporal varying feature figure. Afterward, the combined saliency feature map is measured by taking spatial and temporal feature images into account. Finally, the small moving targets are segmented employing an adaptive threshold approach. Abundant quantitative and qualitative experimental results have demonstrated that the introduced approach is remarkable and has a superior accuracy in terms of performance on both public and real datasets in comparison to the state-of-the-art spatial and temporal models.INDEX TERMS Infrared sequence, Small moving target, Sample consensus, Low signal to noise ratio, Spatial and temporal feature model.

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An Adaptive Vector-Based Vehicles Detection for Urban Intersection Camera Sensors Under Nighttime Illumination

Zhang

Zhao

et al. 2022

IEEE Sensors J.

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Adaptive Vector-Based Sample Consensus Model for Moving Target Detection in Infrared Video

Cited by 5 publications

References 15 publications

Research on Railway Obstacle Detection Method Based on Developed Euclidean Clustering

Research on Railway Obstacle Detection Method Based on Developed Euclidean Clustering

Infrared Detection of Small-Moving Targets Using Spatial Local Vector Difference and Temporal Sample Consensus Measures

An Adaptive Vector-Based Vehicles Detection for Urban Intersection Camera Sensors Under Nighttime Illumination

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